scholarly journals Networks of enhancers and microRNAs drive variation in cell states

2019 ◽  
Author(s):  
Meenakshi Chakraborty ◽  
Sofia Hu ◽  
Marco Del Giudice ◽  
Andrea De Martino ◽  
Carla Bosia ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Feedback Loop ◽  
Embryonic Stem ◽  
Developmental Expression ◽  
Protein Coding ◽  
Pluripotency Factors ◽  
Gene Regulatory ◽  
Cell Variation ◽  
Pluripotency Genes

AbstractCell-to-cell variation in gene expression is a common feature of developmental processes. Yet, it remains unclear whether molecular mediators can generate variation and how this process is coordinated across loci to allow the emergence of new cell states. Using embryonic stem cells (ESCs) as a model of development, we found interconverting cell states that resemble developmental expression programs and vary in activity at specific enhancers, such as those regulating pluripotency genes Nanog and Sox2 but not Pou5f1 (Oct4). Variable enhancers drive expression of variable genes, including those encoding microRNAs (miRNAs). Notably, variable miRNAs increase cell-to-cell variation by acting on neighborhoods of pluripotency genes. The encoded, variable pluripotency factors bind variable enhancers, forming a feedback loop that amplifies variation and allows the emergence of new cell states. These findings suggest gene regulatory networks composed of enhancers, protein-coding genes, and miRNAs harness inherent variation into developmental outcomes.

scholarly journals Widespread dysregulation of long non-coding genes associated with fatty acid metabolism, cell division, and immune response gene networks in xenobiotic-exposed rat liver

2019 ◽  
Author(s):  
Kritika Karri ◽  
David J. Waxman
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Fatty Acid ◽  
Cell Division ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Acid Metabolism ◽  
Protein Coding ◽  
Xenobiotic Exposure ◽  
Gene Regulatory

AbstractXenobiotic exposure activates or inhibits transcription of hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about the effects of xenobiotic exposure on long noncoding RNAs (lncRNAs), many of which play critical roles in regulating gene expression. Objective: to develop a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We analyzed 115 liver RNA-seq data sets from male rats treated with 27 individual chemicals representing seven mechanisms of action (MOAs) to assemble the long non-coding transcriptome of xenobiotic-exposed rat liver. Ortholog analysis was combined with co-expression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered >1,400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs were often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by at least 10 chemicals, and 5 xeno-lncs responded to at least 20 of the 27 chemicals investigated. 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc–protein-coding gene co-expression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, and immune response pathways. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression in cis or in trans. This work elucidates extensive networks of xeno-lnc–protein-coding gene interactions and provides a framework for understanding the extensive transcriptome-altering actions of diverse foreign chemicals in a key responsive mammalian tissue.

scholarly journals Ethanol Interferes with Gene Regulatory Networks of Embryonic Stem Cells

2011 ◽  
Vol 25 (S1) ◽  
Author(s):  
Rosa Sanchez‐Alvarez ◽  
Saurabh Gayen ◽  
Rajanikanth Vadigepalli ◽  
Helen Anni
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Gene Regulatory

scholarly journals Multiview Graph Learning for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Abdullah Karaaslanli ◽  
SATABDI SAHA ◽  
Selin Aviyente ◽  
Tapabrata Maiti
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Joint Estimation ◽  
Embryonic Stem Cell Differentiation ◽  
Graph Learning ◽  
Gene Regulatory ◽  
Cell Gene

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

scholarly journals Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sumin Jang ◽  
Sandeep Choubey ◽  
Leon Furchtgott ◽  
Ling-Nan Zou ◽  
Adele Doyle ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Cell Fate Decisions ◽  
Transcriptomics Data ◽  
Gene Regulatory

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.

scholarly journals PCL2 modulates gene regulatory networks controlling self-renewal and commitment in embryonic stem cells

Cell Cycle ◽  
2011 ◽  
Vol 10 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Emily Walker ◽  
Janet L. Manias ◽  
Wing Y. Chang ◽  
William L. Stanford
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Self Renewal ◽  
Gene Regulatory

scholarly journals Non-Coding RNAs and their Integrated Networks

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Peijing Zhang ◽  
Wenyi Wu ◽  
Qi Chen ◽  
Ming Chen
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Protein Coding ◽  
Integrated Networks ◽  
Cellular Processes ◽  
Sequencing Technologies ◽  
Different Types ◽  
Gene Regulatory ◽  
Non Coding Rnas ◽  
Eukaryotic Genomes

AbstractEukaryotic genomes are pervasively transcribed. Besides protein-coding RNAs, there are different types of non-coding RNAs that modulate complex molecular and cellular processes. RNA sequencing technologies and bioinformatics methods greatly promoted the study of ncRNAs, which revealed ncRNAs’ essential roles in diverse aspects of biological functions. As important key players in gene regulatory networks, ncRNAs work with other biomolecules, including coding and non-coding RNAs, DNAs and proteins. In this review, we discuss the distinct types of ncRNAs, including housekeeping ncRNAs and regulatory ncRNAs, their versatile functions and interactions, transcription, translation, and modification. Moreover, we summarize the integrated networks of ncRNA interactions, providing a comprehensive landscape of ncRNAs regulatory roles.

scholarly journals The molecular logic of Nanog-induced self-renewal

2018 ◽  
Author(s):  
Victor Heurtier ◽  
Nick Owens ◽  
Inma Gonzalez ◽  
Florian Mueller ◽  
Caroline Proux ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Chromatin Accessibility ◽  
Self Renewal ◽  
Pluripotency Factors ◽  
Molecular Logic ◽  
Repressive Histone Mark ◽  
Gene Regulatory

Transcription factor networks, together with histone modifications and signalling pathways, underlie the establishment and maintenance of gene regulatory architectures associated with the molecular identity of each cell type. However, how master transcription factors individually impact the epigenomic landscape and orchestrate the behaviour of regulatory networks under different environmental constraints is only very partially understood. Here, we show that the transcription factor Nanog deploys multiple distinct mechanisms to enhance embryonic stem cell self-renewal. In the presence of LIF, which fosters self-renewal, Nanog rewires the pluripotency network by promoting chromatin accessibility and binding of other pluripotency factors to thousands of enhancers. In the absence of LIF, Nanog blocks differentiation by sustaining H3K27me3, a repressive histone mark, at developmental regulators. Among those, we show that the repression of Otx2 plays a preponderant role. Our results underscore the versatility of master transcription factors, such as Nanog, to globally influence gene regulation during developmental processes.

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